Industry Solutions

Health Care

Reduce the number of falls in hospitals

Hospitals started using AI to reduce the number of patients who suffer from dangerous falls during
their hospitalization

Source: USNews

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Challenge

Falls that occur in hospitalized patients are a widespread and serious
threat
to patient safety. El Camino Hospital in Silicon Valley set to reduce the number of
patient
suffering from dangerous falls during their hospitalization.

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Solution

A software that predicts which patients are most likely to fall by deriving
from
Electronic Health Records (EHR) who are the patients with risk factors to fall and
improving
the
predictive model with real-time tracking of patients.

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Impact

Six months after the hospital implemented artificial intelligence
technology,
the
rate at which patients suffered dangerous falls dropped 39 %.

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Challenge

The management of Lucile Packard Children's Hospital in Palo Alto were
interested to predict the likelihood of delays to surgeries. Through this
distribution of delay-likelihoods, decision makers could be prompted with options to
adjust the schedule.

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Solution

Implemented real-time prediction platform that allows real-time
information about the provided treatments. Due to the real-time information
regarding a surgery delay or a predicted run over decision maker can now act
proactively versus reactively. Used data included the type of procedure, the
surgeon's historical record and the patient's medical history.

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Impact

10% reduction in case overruns and 15% decrease in case under-runs
within four months of implementing the software. The percentage of cases delayed by
more than 10 minutes decreased by 11 %, cumulatively resulting in 520 hours of
delays prevented since the launch.

Industry Solutions

Banking

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Challenge

As banks go digital they face new levels of fraud, or fraud threats that
have to be checked at very high speed. Often the major cost is turning away good
business because fraud detection is too restrictive and turns down lucrative deals.
Denmark’s Danske Bank was automatically identifying 1,200 potential frauds per day in
its transaction monitoring whereas 99.5 % of them were falsely identified

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Solution

after reviewing some of the anti-fraud software packages, the bank decided
to build its own predictive model to better customise for their data landscape. The
prediction model hereby leveraged the numerous data sources within the bank.

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Impact

With a machine learning solution, the bank was able to reduce falsely
identified frauds by 35 % and improve detection of actual frauds at roughly the same
percent. When the solution was extended to base on deep learning, the numbers almost
doubled to a 60 % reduction in mistakenly identified frauds and about 50% improvement in
detecting actual fraud

Automated filing system

Thanks to AI technology JPMorgan Chase saved 360 thousand work hours of lawyers with an automated
system able to accomplish the same task at higher quality within seconds.

Source: Futurism

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Challenge

The bank had to spend 360,000 hours of work each year by lawyers and
loan officers tackling a slew of rather mundane tasks, such as interpreting
commercial-loan agreements for filing purposes.

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Solution

An AI software was developed to parse financial deals that once kept
legal teams busy for thousands of hours. It was trained on the results of real
lawyers on many past loan agreements and now interprets them quicker than human
lawyers.

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Impact

The software has helped reduce loan-servicing mistakes that were often
attributable to human error in interpreting 12,000 new contracts per year.

Industry Solutions

Insurance

Optimise insurance cost and pricing for large-loss cases

Source: Google

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Challenge

Approximately 7-10% of AXA’s customers cause a car accident every year.
While most of these are small accidents involving insurance payments in the hundreds or
thousands of dollars, about 1% are so-called large-loss cases, that require pay-outs
over $10,000. It was important for AXA to understand which clients are at higher risk
for such cases in order to optimize the pricing of its policies.

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Solution

AXA’s R&D team in Japan initially developed a machine learning model to
predict if a driver may cause a large-loss case during the insurance period. First, the
team implemented a traditional machine-learning technique, called Random Forest, which
led to a prediction accuracy comparable to random chance. The team then improved the
accuracy by developing an experimental deep learning (neural-network) model and achieved
78% accuracy in its predictions.

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Impact

The model achieved 78% accuracy in its predictions. This improvement could
give AXA a significant advantage for optimizing insurance cost and pricing, in addition
to the possibility of creating new insurance services such as real-time pricing at point
of sale.

Detecting fraud and abuse

Increasing the precision of fraud detection systems.

Source Elderresearch

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Challenge

Investigating health providers suspected of billing for fraudulent
procedures can be a costly and time-consuming process, since there are relatively
few bad actors: a typical needle-in-a-haystack problem.

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Solution

A prediction model that scores and ranks cases by risk to direct
investigators to those cases with the highest potential for fraud

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Impact

Increased the fraud detection rate from 5% to 48% for the top 50
riskiest providers identified by the model. Hereby increased efficiency of
investigative resources.

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